The Regeneron Science Talent Search (STS), the nation’s oldest and most prestigious science and mathematics competition for high school seniors, named Jason Youm, a high school senior who joined MSEAS during summer 2024 as an RSI scholar, among the top 300 scholars out of nearly 2,500 in the competition. Each scholar will receive $2,000, and their schools will also receive $2,000 to use toward STEM-related activities. Jason’s research with MSEAS was on “MSEAS-ParEq for Ocean Acoustic Modeling in the New England Seamount,” and was advised by Marcoul Robin. His Regeneron STS project title was “Average Rényi Entanglement Entropy in Gaussian Boson Sampling.”

This is an extraordinary accomplishment deserving of much celebration, so congrats Jason!

Congratulations to Dr. Bastien Schnitzler on his graduation! Bastien, a visiting student at MSEAS during spring/summer 2024, successfully defended and received his PhD from the National School of Aeronautics and Space (ISAE) and University of Toulouse for his research on “Trajectory Optimization for Long-Range Light Vehicles in Unsteady Flow Fields with Obstacles, Diffuse Hazard and Uncertainty.” We wish all the best to Bastien on plotting his future, hazard-free path!

Machine learning techniques are applied to Lagrangian trajectory reconstructions, which are important in oceanography for providing guidance to search and rescue efforts, forecasting the spread of harmful algal blooms, and tracking pollutants and marine debris. This study evaluates the ability of two types of neural networks for learning ocean trajectories from nearly 250 surface drifters released during the Grand Lagrangian Deployment in the Gulf of Mexico from Jul-Oct 2012. First, simple fully connected neural networks were trained to predict an individual drifter’s trajectory over 24 h and 5 d time windows using only that drifter’s previous velocity time series. These networks, despite having successfully learned modeled trajectories in a previous study, failed to outperform common autoregressive models in any of the tests conducted. This was true even when drifters were pre-sorted into geospatial groups based on past trajectories and different networks were trained on each group to reduce the variability that each network had to learn. In contrast, a more sophisticated social spatio-temporal graph convolutional neural network (STN), originally developed for learning pedestrian trajectories, demonstrated greater potential due to two important features: learning spatial and temporal patterns simultaneously, and sharing information between similarly-behaving drifters to facilitate the prediction of any particular drifter. Position prediction errors averaged around 60 km at day 5, roughly 20 km lower than autoregression, and even better for certain subsets of drifters. The passage of Tropical Cyclone Isaac over the drifter array as a tropical storm and Category 1 hurricane provided a unique opportunity to also explore whether these models would benefit from adding wind as a predictor when making short 24 h predictions. The STNs were found to not benefit from wind on average, though certain subsets of drifters exhibited slightly lower reconstruction errors at hour 24 with the addition of wind.

Recently, an article “Surface-based sonar system could rapidly map the ocean floor at high resolution” appeared in MIT News that highlights MSEAS research as part of the Wide Area Ocean Floor Mapping project, sponsored by MIT Lincoln Lab. This research was also recognized by 2024 R&D 100 Awards, sometimes called the “Oscars of Innovation.” Congrats again to Aaron, Wael, Pat, and Chris!

We are pleased to announce that Melody Yu, a high school senior who joined MSEAS during summer 2024 as an RSI scholar, was recently admitted (early) to MIT for the Fall 2025 semester. Congrats Melody!